Abstract

A novel technique by which wireless devices such as sensor nodes can deduce their own mobility status,based on analysis of patterns in their local neighbourhood, is described.For many systems in which a neighbour table is maintained through regular status messages or other interaction, the technique incurs no additional communication overhead. The technique does not require that nodes have additional information such as absolute or relative locations, or neighbourhood node density.The work considers systems with heterogeneous time-variant mobility models, in which a subset of nodes follows a random walk mobility model, another subset follows a random waypoint mobility model (i.e.have intermittent movement), some nodes have group mobility and there is a static subset.We simulate these heterogeneous mobility systems and evaluate the performance of the Self-Detection of Mobility Status algorithm (SDMS) against a number of metrics and in a wide variety of system configurations.